Escobar Castillejos, David
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Escobar Castillejos, David
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Escobar Castillejos, David
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descobarc
Escobar-Castillejos, David
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ORCID
Scopus Author ID
57130992800
Researcher ID
I-1228-2019

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Learning Analytics to Determine Profile Dimensions of Students Associated with Their Academic Performance

2022 , Gonzalez-Nucamendi, Andres , Noguez, Julieta , Neri, Luis , Robledo-Rella, Víctor , García-Castelán, Rosa María Guadalupe , Escobar Castillejos, David

With the recent advancements of learning analytics techniques, it is possible to build predictive models of student academic performance at an early stage of a course, using student’s self-regulation learning and affective strategies (SRLAS), and their multiple intelligences (MI). This process can be conducted to determine the most important factors that lead to good academic performance. A quasi-experimental study on 618 undergraduate students was performed to determine student profiles based on these two constructs: MI and SRLAS. After calibrating the students’ profiles, learning analytics techniques were used to study the relationships among the dimensions defined by these constructs and student academic performance using principal component analysis, clustering patterns, and regression and correlation analyses. The results indicate that the logical-mathematical intelligence, intrinsic motivation, and self-regulation have a positive impact on academic performance. In contrast, anxiety and dependence on external motivation have a negative effect on academic performance. A priori knowledge of the characteristics of a student sample and its likely behavior predicted by the models may provide both students and teachers with an early-awareness alert that can help the teachers in designing enhanced proactive and strategic decisions aimed to improve academic performance and reduce dropout rates. From the student side, knowledge about their main academic profile will sharpen their metacognition, which may improve their academic performance.

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The prediction of academic performance using engineering student’s profiles

2021 , Gonzalez-Nucamendi, Andres , Noguez, Julieta , Neri, Luis , Robledo-Rella, Víctor , García-Castelán, Rosa María Guadalupe , Escobar Castillejos, David

This article describes the determination of student profiles based on the constructs of multiple intelligences and on learning and affective strategies, in order to identify the most important characteristics for ensuring the academic success of engineering students. The two constructs were organized in terms of eight dimensions each: the basis for developing two questionnaires that were completed by 618 undergraduate engineering students, in an attempt to define their student profile. Three alternative measures were designed to determine numerical values for each dimension, according to their capacity to predict academic performance in terms of final grades, using regression analysis. According to the study's findings, the logical/mathematical dimension plays an important role in student performance, while anxiety has a negative effect on final grades. The definition of appropriate measures to determine students’ cognitive, affective, and self-regulatory profiles can provide instructors with timely information to implement appropriate teaching strategies in their groups. © 2021 Computers and Electrical Engineering, Elsevier Ltd.

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